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Artificial Neuron Using Vertical Mos2/Graphene Threshold www.nature.com/scientificreports OPEN Artifcial Neuron using Vertical MoS2/Graphene Threshold Switching Memristors Received: 11 June 2018 Hirokjyoti Kalita1,2, Adithi Krishnaprasad1,2, Nitin Choudhary1, Sonali Das1, Durjoy Dev1,2, Accepted: 6 November 2018 Yi Ding1,3, Laurene Tetard1,4, Hee-Suk Chung 5, Yeonwoong Jung1,2,3 & Tania Roy1,2,3 Published: xx xx xxxx With the ever-increasing demand for low power electronics, neuromorphic computing has garnered huge interest in recent times. Implementing neuromorphic computing in hardware will be a severe boost for applications involving complex processes such as image processing and pattern recognition. Artifcial neurons form a critical part in neuromorphic circuits, and have been realized with complex complementary metal–oxide–semiconductor (CMOS) circuitry in the past. Recently, metal-insulator- transition materials have been used to realize artifcial neurons. Although memristors have been implemented to realize synaptic behavior, not much work has been reported regarding the neuronal response achieved with these devices. In this work, we use the volatile threshold switching behavior of a vertical-MoS2/graphene van der Waals heterojunction system to produce the integrate-and-fre response of a neuron. We use large area chemical vapor deposited (CVD) graphene and MoS2, enabling large scale realization of these devices. These devices can emulate the most vital properties of a neuron, including the all or nothing spiking, the threshold driven spiking of the action potential, the post-fring refractory period of a neuron and strength modulated frequency response. These results show that the developed artifcial neuron can play a crucial role in neuromorphic computing. Understanding the complex and overwhelming functioning of the human brain has always fascinated scientists and researchers for biological felds and beyond. Te way the human brain can process huge amounts of data and handle pattern recognition with relative ease is immensely compelling and its complexity is continuously evolving. Te building blocks in the brain consist of interconnected neurons and synapses, which cooperate efciently to process incoming signals and decide on outgoing actions. Inspired by the operation of the human brain, various neuromorphic devices, circuits, and systems have been developed that can work analogous to the brain. Owing to the low power dissipation and characteristics that are similar to biological neurons, spiking neural networks (SNN) have garnered huge interests for mimicking and closely resembling the neuro-biological system1. SNNs, the third generation of neural network models, have been found to be more hardware friendly and energy efcient; thus making them more biologically realistic compared to other neural networks2. Te key components of an SNN are artifcial neurons and synapses where the synapses connect the pre-neurons and the post-neurons. SNN uses discrete rather than continuous spikes along with the incorporation of the concept of time. It involves efcient transfer of information based on precise timing of a sequence of spikes. Important synaptic behaviors such as synaptic plasticity, long term potentiation and depression (LTP and LTD), and Spike Time dependent Plasticity (STDP) have been studied using emerging devices such as memristors for information processing, pattern recognition and learning methods of SNN3–5. But, neuronal response has mostly been realized using complex CMOS circuitry, which requires several transistors to mimic a single neuron. Tese circuits involve a large number of active components, increasing the power dissipation and making high density integration dif- cult5–7. To overcome these issues, various emerging devices have been used to emulate a biological neuron behav- 8,9 ior. Insulator to metal transition (IMT) in materials such as vanadium dioxide (VO2) and niobium dioxide 1NanoScience Technology Center, University of Central Florida, Orlando, Florida, 32826, USA. 2Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida, 32816, USA. 3Department of Materials Science and Engineering, University of Central Florida, Orlando, Florida, 32816, USA. 4Department of Physics, University of Central Florida, Orlando, Florida, 32816, USA. 5Analytical Research Division, Korea Basic Science Institute, Jeonju, jeollabuk-do, 54907, South Korea. Hirokjyoti Kalita, Adithi Krishnaprasad and Nitin Choudhary contributed equally. Correspondence and requests for materials should be addressed to T.R. (email: tania. [email protected]) SCIENTIFIC REPORTS | (2019) 9:53 | DOI:10.1038/s41598-018-35828-z 1 www.nature.com/scientificreports/ Figure 1. (a) Representation of a biological neuron from pre-neuron to output. (b) Output spiking of neuron with respect to the input threshold. (c) Conceptual representation of the v-MoS2/graphene memristor-based artifcial neuron. 10 11 (NbO2) , chalcogenide-based phase change materials and magnetoelectric switching of ferromagnets such as 1 bismuth ferrite (BiFeO3) , have been considered to develop various types of artifcial neurons. VO2 has a very low critical temperature of ~340 K above which its switching behavior disappears, severely limiting the operational range of devices and making them unsuitable for on-chip integration8,9,12. In case of phase change materials-based neurons, the degree to which the neuronal characteristics can be tuned with nominal circuitry is limited by the fact that switching properties arise from the physics of crystallization11. For the same reason, the switching speed in phase change neuron is slow. Treshold switching in memristors can be a natural choice for realizing artifcial neurons. However, the exploitation of threshold switching in memristors to realize artifcial neurons is relatively unexplored. Recently, a Ag/SiO2/Au threshold switching memristor (TSM) was used to demonstrate an 13 integrate-and-fre neuron . Te observation of volatile threshold switching in MoS2 stimulates the possibility of 14 employing this phenomenon for the realization of artifcial neurons . However, the switching in a lateral MoS2 flm mediated by the grain boundaries occludes scaling of lateral device dimensions. Hence, realizing such char- acteristics using a vertical structure can play a critical role in improving scaling opportunities. In this paper, we have constructed an integrate-and-fre artifcial neuron by exploiting the threshold switch- ing behavior in CVD-grown vertical MoS2 (v-MoS2) layers. A novel device structure was fabricated by growing v-MoS2 on a CVD-graphene monolayer as the bottom electrode and with Ni as the top contact to v-MoS2. Te key characteristics of a biological neuron including an all or nothing spiking, a threshold driven spiking, a post fring refractory period and an input strength modulated frequency response were observed. Te developed v-MoS2/ graphene TSM artifcial neuron can operate over a wide range of temperature and can potentially be scaled down to nanometer scale cross bar structures. Tis platform provides an attractive prospect for neuromorphic circuits, which can be seamlessly integrated with existing fabrication technologies enabling large scale realization of such devices. Te schematic of a biological neuron connected to multiple synapses is shown in Fig. 1a. Biological neurons are connected to each other through synapses. Depending upon the input signals received by the dendrites, an action potential (also referred to as a neuron spike) is generated by the soma of the neuron, which is then passed by the axon to other neurons through synapses. Te electrical equilibrium is maintained by the movement of vari- ous ions such as Na+, K+, Cl− and Ca2+ in and out of the neuron cell. Te potential diference between the interior 1 and the exterior of the neuron is referred to as the membrane potential . As the membrane potential (VM) builds 15,16 up, the neuron produces an output spike (Iout) when the potential reaches a particular threshold voltage (Vth) . Tis phenomenon of neuron spiking is shown in Fig. 1b. Te neuron does not fre immediately afer producing a spike even if it keeps receiving input signals. Tis period is known as the refractory period of a neuron8,17. Afer the refractory period, the neuron starts integrating the input again and prepares for the following spike. One of the most commonly used neuron models in computational neuroscience is the integrate-and-fre (IF) model18. An IF artifcial neuron fres as a function of the electrical potential developed across it, thus mimicking the crucial behavior of a biological neuron. Here, this behavior has been realized with a v-MoS2/graphene TSM as shown in the conceptual scheme in Fig. 1c. Te v-MoS2/graphene neuron integrates the input signals received with the help of a capacitor. Te capacitor integrates the charge and as soon as the voltage across the capacitor increases beyond the threshold value of the TSM, the neuron fres and an output spike is produced. Te device schematic of the v-MoS2/graphene device is shown in Fig. 2a. It consists of a CVD-grown mon- 19 olayer graphene, wet transferred on the Si/SiO2 substrate , followed by patterned growth of v-MoS2 on graphene. Nickel contacts were deposited on the graphene and on the v-MoS2. Te optical image of the device is shown SCIENTIFIC REPORTS | (2019) 9:53 | DOI:10.1038/s41598-018-35828-z 2 www.nature.com/scientificreports/ Figure 2. (a) Schematic of a v-MoS2/graphene TSM. (b) Optical image of the MoS2/graphene TSM. (c) A representative picture of
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